Comparative Analysis of Control Barrier Functions and Artificial Potential Fields for Obstacle Avoidance
Andrew Singletary, Karl Klingebiel, Joseph Bourne, Andrew Browning, Phil Tokumaru, Aaron Ames
Abstract
Artificial potential fields (APFs) and their variants have been a staple for collision avoidance of mobile robots and manipulators for almost 40 years. Its model-independent nature, ease of implementation, and real-time performance have played a large role in its continued success over the years. Control barrier functions (CBFs), on the other hand, are a more recent development, commonly used to guarantee safety for nonlinear systems in real-time in the form of a filter on a nominal controller. In this paper, we address the connections between APFs and CBFs. At a theoretic level, we show that given a broad class of APFs, one can construct a CBF that guarantees safety. Additionally, we prove that CBFs obtained from these APFs have additional beneficial properties and can be applied to nonlinear systems. Practically, we compare the performance of APFs and CBFs in the context of obstacle avoidance on simple illustrative examples and for a quadrotor with unknown dynamics, both in simulation and on hardware using onboard sensing.
BibTeX
@inproceedings{iros2021_comparativeanaly,
title = {Comparative Analysis of Control Barrier Functions and Artificial Potential Fields for Obstacle Avoidance},
author = {Andrew Singletary and Karl Klingebiel and Joseph Bourne and Andrew Browning and Phil Tokumaru and Aaron Ames},
booktitle = {IROS 2021},
year = {2021}
}